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Elasticsearch Server - Third Edition
Elasticsearch Server - Third Edition

Elasticsearch Server - Third Edition: Leverage Elasticsearch to create a robust, fast, and flexible search solution with ease , Third Edition

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Profile Icon Marek Rogozinski Profile Icon Rafal Kuc
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Paperback Feb 2016 556 pages 3rd Edition
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Elasticsearch Server - Third Edition

Chapter 2. Indexing Your Data

In the previous chapter, we learned what full text search is and how Apache Lucene fits there. We were introduced to the basic concepts of Elasticsearch and we are now familiar with its top-level architecture, so we know how it works. We used the REST API to index data, to update it, to delete it, and of course to retrieve it. We searched our data with the simple URI query and we used versioning that allowed us to use optimistic locking functionality. By the end of this chapter, you will have learned the following topics:

  • Basic information about Elasticsearch indexing
  • Adjusting Elasticsearch schema-less behavior
  • Creating your own mappings
  • Using out of the box analyzers
  • Configuring your own analyzers
  • Index data in batches
  • Adding additional internal information to indices
  • Segment merging
  • Routing

Elasticsearch indexing

So far we have our Elasticsearch cluster up and running. We also know how to use Elasticsearch REST API to index our data, we know how to retrieve it, and we also know how to remove the data that we no longer need. We've also learned how to search in our data by using the URI request search and Apache Lucene query language. However, until now we've used Elasticsearch functionality that allows us not to care about indices, shards, and data structure. This is not something that you may be used to when you are coming from the world of SQL databases, where you need the database and the tables with all the columns created upfront. In general, you needed to describe the data structure to be able to put data into the database. Elasticsearch is schema-less and by default creates indices automatically and because of that we can just install it and index data without the need of any preparations. However, this is usually not the best situation when it comes to production...

Mappings configuration

If you are used to SQL databases, you may know that before you can start inserting the data in the database, you need to create a schema, which will describe what your data looks like. Although Elasticsearch is a schema-less (we rather call it data driven schema) search engine and can figure out the data structure on the fly, we think that controlling the structure and thus defining it ourselves is a better way. The field type determining mechanism is not going to guess the future. For example, if you first send an integer value, such as 60, and you send a float value such as 70.23 for the same field, an error can happen or Elasticsearch will just cut off the decimal part of the float value (which is actually what happens). This is because Elasticsearch will first set the field type to integer and will try to index the float value to the integer field which will cause cutting of the decimal point in the floating point number. In the next few pages you'll see...

Batch indexing to speed up your indexing process

In Chapter 1, Getting Started with Elasticsearch Cluster, we saw how to index a particular document into Elasticsearch. It required opening an HTTP connection, sending the document, and closing the connection. Of course, we were not responsible for most of that as we used the curl command, but in the background this is what happened. However, sending the documents one by one is not efficient. Because of that, it is now time to find out how to index a large number of documents in a more convenient and efficient way than doing so one by one.

Preparing data for bulk indexing

Elasticsearch allows us to merge many requests into one package. This package can be sent as a single request. What's more, we are not limited to having a single type of request in the so called bulk – we can mix different types of operations together, which include:

  • Adding or replacing the existing documents in the index (index)
  • Removing documents from the index...

Introduction to segment merging

In the Full text searching section of Chapter 1, Getting Started with Elasticsearch Cluster, we mentioned segments and their immutability. We wrote that the Lucene library, and thus Elasticsearch, writes data to certain structures that are written once and never change. This allows for some simplification, but also introduces the need for additional work. One such example is deletion. Because segment, cannot be altered, information about deletions must be stored alongside and dynamically applied during search. This is done by filtering deleted documents from the returned result set. The other example is the inability to modify the documents (however, some modifications are possible, such as modifying numeric doc values). Of course, one can say that Elasticsearch supports document updates (refer to the Manipulating data with the REST API section of Chapter 1, Getting Started with Elasticsearch Cluster). However, under the hood, the old document is marked as...

Introduction to routing

By default, Elasticsearch will try to distribute your documents evenly among all the shards of the index. However, that's not always the desired situation. In order to retrieve the documents, Elasticsearch must query all the shards and merge the results. What if we could divide our data on some basis (for example, the client identifier) and use that information to put data with the same properties in the same place in the cluster. Elasticsearch allows us to do that by exposing a powerful document and query distribution control mechanism routing. In short, it allows us to choose a shard to be used to index or search the data.

Default indexing

During indexing operations, when you send a document for indexing, Elasticsearch looks at its identifier to choose the shard in which the document should be indexed. By default, Elasticsearch calculates the hash value of the document's identifier and, on the basis of that, it puts the document in one of the available...

Elasticsearch indexing


So far we have our Elasticsearch cluster up and running. We also know how to use Elasticsearch REST API to index our data, we know how to retrieve it, and we also know how to remove the data that we no longer need. We've also learned how to search in our data by using the URI request search and Apache Lucene query language. However, until now we've used Elasticsearch functionality that allows us not to care about indices, shards, and data structure. This is not something that you may be used to when you are coming from the world of SQL databases, where you need the database and the tables with all the columns created upfront. In general, you needed to describe the data structure to be able to put data into the database. Elasticsearch is schema-less and by default creates indices automatically and because of that we can just install it and index data without the need of any preparations. However, this is usually not the best situation when it comes to production environments...

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Key benefits

  • Boost the searching capabilities of your system through synonyms, multilingual data handling, nested objects and parent-child documents
  • Deep dive into the world of data aggregation and data analysis with ElasticSearch
  • Explore a wide range of ElasticSearch modules that define the behavior of a cluster

Description

ElasticSearch is a very fast and scalable open source search engine, designed with distribution and cloud in mind, complete with all the goodies that Apache Lucene has to offer. ElasticSearch’s schema-free architecture allows developers to index and search unstructured content, making it perfectly suited for both small projects and large big data warehouses, even those with petabytes of unstructured data. This book will guide you through the world of the most commonly used ElasticSearch server functionalities. You’ll start off by getting an understanding of the basics of ElasticSearch and its data indexing functionality. Next, you will see the querying capabilities of ElasticSearch, followed by a through explanation of scoring and search relevance. After this, you will explore the aggregation and data analysis capabilities of ElasticSearch and will learn how cluster administration and scaling can be used to boost your application performance. You’ll find out how to use the friendly REST APIs and how to tune ElasticSearch to make the most of it. By the end of this book, you will have be able to create amazing search solutions as per your project’s specifications.

Who is this book for?

If you are a competent developer and want to learn about the great and exciting world of ElasticSearch, then this book is for you. No prior knowledge of Java or Apache Lucene is needed.

What you will learn

  • Configure, create, and retrieve data from your indices
  • Use an ElasticSearch query DSL to create a wide range of queries
  • Discover the highlighting and geographical search features offered by ElasticSearch
  • Find out how to index data that is not flat or data that has a relationship
  • Exploit a prospective search to search for queries not documents
  • Use the aggregations framework to get more from your data and improve your client's search experience
  • Monitor your cluster state and health using the ElasticSearch API as well as third-party monitoring solutions
  • Discover how to properly set up ElasticSearch for various use cases
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Table of Contents

12 Chapters
1. Getting Started with Elasticsearch Cluster Chevron down icon Chevron up icon
2. Indexing Your Data Chevron down icon Chevron up icon
3. Searching Your Data Chevron down icon Chevron up icon
4. Extending Your Querying Knowledge Chevron down icon Chevron up icon
5. Extending Your Index Structure Chevron down icon Chevron up icon
6. Make Your Search Better Chevron down icon Chevron up icon
7. Aggregations for Data Analysis Chevron down icon Chevron up icon
8. Beyond Full-text Searching Chevron down icon Chevron up icon
9. Elasticsearch Cluster in Detail Chevron down icon Chevron up icon
10. Administrating Your Cluster Chevron down icon Chevron up icon
11. Scaling by Example Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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